Sparse Aperture InISAR Imaging via Sequential Multiple Sparse Bayesian Learning
Sensors, 2017 Interferometric inverse synthetic aperture radar (InISAR) imaging for sparse-aperture (SA) data is still a challenge, because the similarity and matched degree between ISAR images from different channels are destroyed by the SA data.
Shuanghui Zhang, Yongxiang Liu, Xiang Li
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Learned-SBL-GAMP based hybrid precoders/combiners in millimeter wave massive MIMO systems. [PDF]
PLoS ONE, 2023 In Millimeter-Wave (mm-Wave) massive Multiple-Input Multiple-Output (MIMO) systems, hybrid precoders/combiners must be designed to improve antenna gain and reduce hardware complexity.
Shoukath Ali K +4 more
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High-Accuracy Off-Grid Sparse Bayesian Learning with Reliability-Guided Inference for Direction-of-Arrival Estimation [PDF]
SensorsOff-grid direction-of-arrival (DOA) estimation based on sparse Bayesian learning (SBL) can alleviate angular discretization mismatch, but its practical performance may be affected by unreliable posterior relevance statistics, sensitivity of effective ...
Wenchao He +3 more
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Compressive Sensing via Variational Bayesian Inference under Two Widely Used Priors: Modeling, Comparison and Discussion [PDF]
Entropy, 2023 Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying ...
Mohammad Shekaramiz, Todd K. Moon
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Physiological swelling imaging in human calf under stocking compression by sparse Bayesian learning implemented into electrical impedance tomography (SBL-EIT) [PDF]
Biomedical Engineering LettersAbstract Physiological swelling in human calf has been imaged under stocking compression by the sparse Bayesian learning implemented into electrical impedance tomography (SBL-EIT) to evaluate the in situ treatment effect of various compression pressures. SBL-EIT reconstructs conductivity distribution
Kota Asano +4 more
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Passive Sonar Target Identification Using Multiple-Measurement Sparse Bayesian Learning [PDF]
Sensors, 2022 Accurate estimation of the frequency component is an important issue to identify and track marine objects (e.g., surface ship, submarine, etc.). In general, a passive sonar system consists of a sensor array, and each sensor receives data that have common
Myoungin Shin +3 more
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Velocity Estimation of Passive Target Based on Sparse Bayesian Learning Cross-Spectrum [PDF]
SensorsTo solve the poor performance or even failure of the cross-spectrum (CS) method in hydroacoustic weak-target passive velocimetry, a sparse Bayesian learning cross-spectrum method (SBL-CS), combining phase compensation with sparse Bayesian learning (SBL ...
Xionghui Li +3 more
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Direction-of-Arrival Estimation via Sparse Bayesian Learning Exploiting Hierarchical Priors with Low Complexity [PDF]
SensorsFor direction-of-arrival (DOA) estimation problems in a sparse domain, sparse Bayesian learning (SBL) is highly favored by researchers owing to its excellent estimation performance.
Ninghui Li +3 more
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DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning [PDF]
SensorsThrough extensive literature review, it has been found that sparse Bayesian learning (SBL) is mainly applied to traditional scalar hydrophones and is rarely applied to vector hydrophones.
Hongyan Wang +6 more
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Prediction of tablet disintegration time based on formulations properties via artificial intelligence by comparing machine learning models and validation [PDF]
Scientific ReportsThis research assesses multiple predictive models aimed at estimating disintegration time for pharmaceutical oral formulations, based on a dataset comprising nearly 2,000 data points that include molecular, physical, compositional, and formulation ...
Mohammed Ghazwani, Umme Hani
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